Mohan Mood Postdoctoral research associate Contact 865.576.2576 | MOODM@ORNL.GOV All Publications Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents Physics-Informed Machine Learning to Predict Solvatochromic Parameters of Designer Solvents with Case Studies in CO2 and Lignin Dissolution Macroscale properties and atomic-scale mechanisms of ash removal in low-temperature hydrothermal carbonization Molecular-level design of alternative media for energy-saving pilot-scale fibrillation of nanocellulose High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning... Inhibition of asphaltene aggregation using deep eutectic solvents: COSMO-RS calculations and experimental validation Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents Multiscale investigation of the mechanism of biomass deconstruction in the dimethyl isosorbide/water Co-solvent pretreatment system Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids Accurate prediction of carbon dioxide capture by deep eutectic solvents using quantum chemistry and a neural network Key Links
